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Automatic security inspection relying on computer vision technology is achallenging task in real-world scenarios due to many factors, such asintra-class variance, class imbalance, and occlusion. Most previous methodsrarely touch the cases where the prohibited items are deliberately hidden inmessy objects because of the scarcity of large-scale datasets, hindering theirapplications. To address this issue and facilitate related research, we presenta large-scale dataset, named PIDray, which covers various cases in real-worldscenarios for prohibited item detection, especially for deliberately hiddenitems. In specific, PIDray collects 124,486 X-ray images for $12$ categories ofprohibited items, and each image is manually annotated with careful inspection,which makes it, to our best knowledge, to largest prohibited items detectiondataset to date. Meanwhile, we propose a general divide-and-conquer pipeline todevelop baseline algorithms on PIDray. Specifically, we adopt the tree-likestructure to suppress the influence of the long-tailed issue in the PIDraydataset, where the first course-grained node is tasked with the binaryclassification to alleviate the influence of head category, while thesubsequent fine-grained node is dedicated to the specific tasks of the tailcategories. Based on this simple yet effective scheme, we offer strongtask-specific baselines across object detection, instance segmentation, andmulti-label classification tasks and verify the generalization ability oncommon datasets (e.g., COCO and PASCAL VOC). Extensive experiments on PIDraydemonstrate that the proposed method performs favorably against currentstate-of-the-art methods, especially for deliberately hidden items. Ourbenchmark and codes will be released at https://github.com/lutao2021/PIDray.

PIDray: A Large-scale X-ray Benchmark for Real-World Prohibited Item Detection